2.
Oracle DBA since 1999
OCP 9i,10g,11g
Rac certified Expert
Exadata certified implementation specialist
Blogger since 2012
@bertranddrouvot
BasketBall fan
3.
Some examples of R usage with the oracle
database
From a DBA point of view
Retrieve system statistics/wait events with
some AWR queries
Real time Data
Dashboard of the database activity
5.
Because R is a powerful tool for statistical
analysis with graphing and plotting packages
built in.
Furthermore, R can connect to Oracle via a
JDBC package which makes importing data
very easy.
8. select s.begin_interval_time,sta.stat_name,sta.VALUE,
round(((sta.VALUE)/
(
(extract(day from s.END_INTERVAL_TIME)-extract(day from s.BEGIN_INTERVAL_TIME))*86400 +
(extract(hour from s.END_INTERVAL_TIME)-extract(hour from s.BEGIN_INTERVAL_TIME))*3600 +
(extract(minute from s.END_INTERVAL_TIME)-extract(minute from s.BEGIN_INTERVAL_TIME))*60 +
(extract(second from s.END_INTERVAL_TIME)-extract(second from s.BEGIN_INTERVAL_TIME))
)
),2) VALUE_PER_SEC
from
(
select instance_number,snap_id,stat_name,
value - first_value(value) over (partition by stat_name order by snap_id rows 1 preceding) "VALUE"
from
dba_hist_sysstat
where stat_name like nvl('&stat_name',stat_name)
and instance_number = (select instance_number from v$instance)
) sta, dba_hist_snapshot s
where sta.instance_number=s.instance_number
and sta.snap_id=s.snap_id
and s.BEGIN_INTERVAL_TIME >= trunc(sysdate-&sysdate_nb_day_begin_interval+1)
and s.BEGIN_INTERVAL_TIME <= trunc(sysdate-&sysdate_nb_day_end_interval+1)
order by s.begin_interval_time asc;
12. select e.WAIT_CLASS,e.event_name,s.begin_interval_time,e.TOTAL_WAITS,e.TIME_WAITED_MS,e.TIME_WAITED_MS /
TOTAL_WAITS "MS_PER_WAIT"
from
(
select instance_number,snap_id,WAIT_CLASS,event_name,
total_waits - first_value(total_waits) over (partition by event_name order by snap_id rows 1 preceding)
"TOTAL_WAITS",
(time_waited_micro - first_value(time_waited_micro) over (partition by event_name order by snap_id rows 1
preceding))/1000 "TIME_WAITED_MS"
from
dba_hist_system_event
where
WAIT_CLASS like nvl('&WAIT_CLASS',WAIT_CLASS)
and event_name like nvl('&event_name',event_name)
and instance_number = (select instance_number from v$instance)
) e, dba_hist_snapshot s
where e.TIME_WAITED_MS > 0
and e.instance_number=s.instance_number
and e.snap_id=s.snap_id
and s.BEGIN_INTERVAL_TIME >= trunc(sysdate-&sysdate_nb_day_begin_interval+1)
and s.BEGIN_INTERVAL_TIME <= trunc(sysdate-&sysdate_nb_day_end_interval+1) order by 1
15.
Query is a little bit complicated (comes from
OEM)
16.
17.
18.
19.
# compute percentages
pct <- round(dg_space[,'SIZE_GB']/sum(dg_space[,'SIZE_GB'])*100)
# add %
pct <- paste(pct,"%",sep="")
# Add db size to db_name
db_name_size<-paste(dg_space[,'DB_NAME']," (",sep="")
db_name_size<-paste(db_name_size,dg_space[,'SIZE_GB'],sep="")
db_name_size<-paste(db_name_size," GB)",sep="")
# Set the colors
colors<-rainbow(length(dg_space[,'DB_NAME']))
# Plot
pie(dg_space[,'SIZE_GB'], labels = pct, col=colors, main=paste(dg," Disk Group Usage",sep=""))
# Add a legend
legend(x=1.2,y=0.5,legend=db_name_size,fill=colors,cex=0.8)
20.
Basically the script takes a snapshot based on
the v$sysstat view then computes and graphs
the delta with the previous snapshot.
21.
22.
23. myquery<-"
Select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as DATEEVT, NAME,VALUE,1 VALUE_PER_SEC
from v$sysstat
where name='"
# Keep them
prev_date<<-qoutput[,'DATEEVT']
prev_value<<-qoutput[,'VALUE']
# Launch the loop for the real-time graph
nb_refresh <- as.integer(nb_refresh)
for(i in seq(nb_refresh)) {
# Get the new data
Sys.sleep(refresh_interval)
qoutput<-dbGetQuery(conn,myquery)
# Keep the current value
current_date<-qoutput[,'DATEEVT']
current_value<-qoutput[,'VALUE']
# compute difference between snap for value and value per sec
#qoutput[,'DATEEVT']<-current_date
qoutput[,'VALUE']<-current_value-prev_value
24.
Basically the script takes a snapshot based on
the v$system_event view then computes and
graphs the delta with the previous snapshot.
25.
26. myquery<-"
Select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as
DATEEVT, EVENT,TOTAL_WAITS,TIME_WAITED_MICRO/1000 as TIME_WAITED_MS,1 MS_PER_WAIT
from v$system_event
where event='"
# Keep them
prev_tw<<-qoutput[,'TIME_WAITED_MS']
prev_twaits<<-qoutput[,'TOTAL_WAITS']
# So we want 4 graphs
par(mfrow =c(4,1))
# Launch the loop for the real-time graph
nb_refresh <- as.integer(nb_refresh)
for(i in seq(nb_refresh)) {
# Get the new data
Sys.sleep(refresh_interval)
qoutput<-dbGetQuery(conn,myquery)
# compute difference between snap for time_waited_ms, total_waits and then compute ms_per_wait
qoutput[,'TIME_WAITED_MS']<-current_tw-prev_tw
qoutput[,'TOTAL_WAITS']<-current_twaits-prev_twaits
27. myquery<-"
select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as DATEEVT,
wait_class as WAIT_CLASS,
time_waited_micro/1000 as TIME_WAITED_MS,
EVENT as EVENT
from v$system_event where WAIT_CLASS != 'Idle'
union
select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as DATEEVT,
'CPU' as WAIT_CLASS
,sum(value/1000) as TIME_WAITED_MS
,'CPU' as EVENT
from
v$sys_time_model
where
stat_name IN ('background cpu time', 'DB CPU')
"
30.
Sub-graph for the wait events distribution
of the wait class having the max time
waited during the last snap:
31.
Sub-graph for the wait class distribution
since the script has been launched:
32.
33.
# Split the screen
no_display<-split.screen( figs = c( 2, 1 ) )
# Split the second screen
no_display<-split.screen( figs = c( 1, 2
), screen=2 )
Much more complicated: source code is
available through my blog.
